Sökning: "Stock Market Learning"
Visar resultat 1 - 5 av 97 uppsatser innehållade orden Stock Market Learning.
1. Dynamik och tillförlighet i finansiell prognostisering : En analys av djupinlärningsmodeller och deras reaktion på marknadsmanipulation
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : Under åren har intensiv forskning pågått för att förbättra maskininlärningsmodellers förmåga att förutse marknadsrörelser. Trots detta har det, under finanshistorien, inträffat flera händelser, såsom "Flash-crash", som har påverkat marknaden och haft dramatiska konsekvenser för prisrörelserna. LÄS MER
2. Deep Learning Based Sentiment Analysis
Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background: Text data includes things like customer reviews and complaints,tweets from social media platforms. When analyzing text-based data, the SentimentModel is used. Understanding news headlines, blogs, the stock market, politicaldebates, and film reviews some of the areas where sentiment analysis is used. LÄS MER
3. Predicering av aktiekursutveckling för svenska aktier utifrån konjunkturdata
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This study aims to investigate whether Swedish economic indicators can be used to predict stock market performance on the Stockholm Stock Exchange. The study is expected to contribute to new research in the field and also explore the potential utility of these predictions for individual investors. LÄS MER
4. Predicting the Movement of the S&P 500 Index using Machine Learning
Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionenSammanfattning : Predicting the stock market has been a longstanding topic of interest in financial research. It is regarded as a highly challenging but important task given the vital role the financial markets play in shaping the global economies. In this thesis, the goal is to predict the movement of the S&P 500 Index using machine learning methods. LÄS MER
5. Extraction of Global Features for enhancing Machine Learning Performance
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Data Science plays an essential role in many organizations and industries to become data-driven in their decision-making and workflow, as models can provide relevant input in areas such as social media, the stock market, and manufacturing industries. To train models of quality, data preparation methods such as feature extraction are used to extract relevant features. LÄS MER